We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulati...We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulation characteristics of CB-RLM PAs,a comprehensive objective function is proposed that combines multi-state impedance trajectory constraints with in-band performance deviations.For the saturation and 6 dB power back-off(PBO)states,approximately optimal impedance regions on the Smith chart are derived using impedance constraint circles based on load-pull simulations.These regions are used together with in-band performance deviations(e.g.,saturated efficiency,6 dB PBO efficiency,and saturated output power)for matching network optimization and design.Second,a multi-objective evolutionary algorithm based on decomposition with adaptive weights,neighborhood,and global replacement is integrated with harmonic balance simulations to optimize design parameters and evaluate performance.Finally,to validate the proposed method,a broadband CB-RLM PA operating from 0.6 to 1.8 GHz is designed and fabricated.Measurement results show that the efficiencies at saturation,6 dB PBO,and 8 dB PBO all exceed 43.6%,with saturated output power being maintained at 40.9–41.5 dBm,which confirms the feasibility and effectiveness of the proposed broadband high-efficiency CB-RLM PA optimization and design approach.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
In maize production,the development of density-tolerant and lodging-resistant varieties has made dense planting an effective strategy for achieving high and stable yields,with superior hybrids serving as a prerequisit...In maize production,the development of density-tolerant and lodging-resistant varieties has made dense planting an effective strategy for achieving high and stable yields,with superior hybrids serving as a prerequisite for successful highdensity cultivation.However,the photosynthetic mechanisms underlying improved density tolerance in maize hybrids released across different eras in China remain unclear.This study investigates 40 years of breeding progress toward enhanced photosynthetic traits under varying planting densities and elucidates the physiological and ecological bases of improved density tolerance in maize hybrids.A three-year field experiment was conducted from 2019 to 2021 to compare eight major Chinese hybrids from four decadal cohorts under three planting densities:45,000(D1),67,500(D2),and 90,000(D3)plants ha^(-1).At high density(D3),modern hybrids exhibited a more optimal canopy architecture and superior leaf photosynthetic performance compared to older hybrids,despite a slight reduction in specific leaf nitrogen.Notably,modern hybrids(2000s)were able to maintain higher net photosynthetic rates and photosynthetic nitrogen use efficiency(PNUE)at D3,resulting in the highest grain yield(GY),which was 118.47%greater than that of older hybrids(1970s).Leaf area duration after anthesis,total chlorophyll content,key photosynthetic enzyme activities,and maximum quantum efficiency of PSII photochemistry were all positively correlated with GY.Among these,PNUE showed the strongest correlation with grain yield and thus represents a key indicator for optimizing maize hybrids.Based on these findings,breeders should continue selecting hybrids under high-density and suboptimal conditions,focusing on optimizing population architecture and enhancing photosynthetic capacity while fine-tuning leaf nitrogen status to develop high-yielding,density-tolerant hybrids capable of sustaining long-term increases in maize grain yield.展开更多
Lodging is a primary factor limiting rice grain yield.Achieving synergistic improvements in grain yield and nitrogen use efficiency(NUE)without increasing lodging risk has been a global research priority.In this study...Lodging is a primary factor limiting rice grain yield.Achieving synergistic improvements in grain yield and nitrogen use efficiency(NUE)without increasing lodging risk has been a global research priority.In this study,two rice cultivars-Yongyou 2640(indica–japonica hybrid rice)and Jinxiangyu 1(inbred japonica rice)-were evaluated in field experiments conducted over two growing seasons.Six nitrogen management strategies were implemented:no nitrogen(T1),conventional urea(T2),controlled-release nitrogen(CRN)(T3),reduction of CRN(T4),CRN combined with single basal application of conventional urea(T5),and CRN combined with split applications of conventional urea(T6).Compared with T2,the integrated nitrogen strategies(T5 and T6)increased NUE by 4.89–5.69%and grain yield by 3.41–4.65%.These treatments also enhanced structural integrity of the second basal internode,evidenced by increased carbohydrate content,internode breaking strength,epidermal silicon layer thickness,number of large and small vascular bundles,and thickness of both parenchymatous and mechanical tissues.Concurrently,internode length,bending moment,and lodging index were reduced.Collectively,these findings indicate that integrating CRN with conventional urea improves morphological,mechanical,physicochemical,and anatomical properties of the second basal internode,thereby enhancing stem strength and enabling high yield and NUE without compromising lodging resistance.展开更多
Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting d...Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting density and N rate,and their relationships with grain yield,radiation use efficiency(RUE),and N use efficiency for grain production(NUEg)in rice.A two-year field experiment was conducted with two hybrid varieties under three N levels,0 kg ha^(-1)(N1),90 kg ha^(-1)(N2)and 180 kg ha^(-1)(N3),and two planting densities,22.2 hills m-2(D1)and 33.3 hills m^(-2)(D2).Results showed 3.4%higher yield and 4.4%higher NUEg under N2D2 compared with N3D1.The extinction coefficient for N(K_(N))and light(K_(L))and their ratio(K_(N)/K_(L))at heading stage were significantly influenced by N rate,planting density,and their interaction.K_(N)decreased with the increase of N input or planting density.Compared to N1,K_(N)decreased by 43.5 and 58.8%under N2 and N3,respectively,while K_(N)under D2 decreased by 16.0%compared to D1.Higher K_(L)and K_(N)/K_(L)values occurred under low N rates,with opposite trends under high N rates.Increased planting density led to decreased K_(L)and K_(N)/K_(L)values.N2D2 demonstrated higher K_(L)and K_(N),and thus comparable K_(N)/K_(L),compared to N3D1.Correlation analysis revealed K_(L)negatively correlated with RUE,while K_(N)and K_(N)/K_(L)positively correlated with NUEg.These findings indicate that increasing planting density under reduced N input could maintain rice yield while enhancing resource use efficiency through regulation of canopy light and N distribution.展开更多
This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced...This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.展开更多
UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple...UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple UAVs-mounted IRS(U-IRS),where the data from ground SNs are transmitted to the FC.In practice,energy efficiency(EE)and mission completion time are crucial metrics for evaluating system performance and operational costs.Recognizing their importance during data collection,we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously.To characterize this tradeoff while considering optimization objective consistency,we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE.Due to the non-convex nature of the formulated problem,obtaining optimal solutions is generally challenging.To tackle this issue,we decompose it into three subproblems:UAV-SN association,number of reflecting elements allocation,andUAVtrajectory optimization.An iterative algorithmcombining genetic algorithm,CS-BJ algorithm,and successive convex approximation technique is proposed to solve these sub-problems.Simulation results demonstrate that when the transmitted data amount is 10 and 30Mbits,compared to the static collection benchmark(the UAV hovers directly above each SN),the EE of the proposed method improves by more than 10.4% and 5.2%,while the total mission completion time is reduced by more than 5.4% and 3.3%,respectively.展开更多
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approa...Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization ...In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components,which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-Ⅱ algorithm.The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation,while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction,establishing the hybrid physics-informed data method for WAAM microstructure prediction.The optimized process achieved a deposition rate of 6257 mm3/min,with effective width and average layer height maintained at 10.1 mm and 4.13 mm,respectively.Microstructural optimization produced a fine,uniform,fully equiaxed grain structure with an average grain size of 38μm.These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight,high-performance components in aerospace and transportation sectors.展开更多
In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assiste...In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.展开更多
The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedic...The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.展开更多
Tourism’s link to the Sustainable Development Goals has been a continuing emphasis,adding momentum to longstandingefforts to ensure tourism’s sustainability.Tourism transport is one of the largest sources of anthrop...Tourism’s link to the Sustainable Development Goals has been a continuing emphasis,adding momentum to longstandingefforts to ensure tourism’s sustainability.Tourism transport is one of the largest sources of anthropogenic carbonemissions,driving global ecological change with profound consequences for ecosystem functioning and biodiversity.Large-scale infrastructure projects such as railway expansion are increasingly promoted for their potential to reducetourism-related carbon dioxide emissions,yet their spatial ecological impacts on regional carbon cycles and ecosystemservices remain poorly understood.This study introduces the concept of Tourism Transport Ecological Efficiency(TTEE)to assess the relationship between human infrastructure,carbon emissions,and ecological sustainability.Using panel datafrom China’s railway expansion between 2011 and 2018,the study provides spatially explicit evidence of how transportinfrastructure shapes tourism’s ecological footprint.Results show that non-Eastern regions experienced a greater increasein TTEE(8.7%)compared to Eastern regions(5.5%),highlighting regional disparities in tourism transport ecologicalsustainability.Railway density had a significant positive direct effect on TTEE,particularly pronounced in non-Easternregions.Additionally,a significant indirect effect of railway density in nearby regions was identified.These findings revealthe interconnected ecological impacts of transport systems and underscore the importance of regionally targeted railwayinvestment strategies.By bridging infrastructure development with ecological processes,this study advances understandingof how tourism transport can be aligned with global carbon reduction goals and ecosystem protection.展开更多
Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost input...Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost inputs.This gap can lead to increased uncertainties in restoration planning.Here we investigated forest dynamics in China's Upper Yangtze River Basin(UYRB)using kernel Normalized Difference Vegetation Index(kNDVI),Leaf Area Index(LAI),Gross Primary Productivity(GPP),Ku-band Vegetation Optical Depth(Ku-VOD)time series and climate data from1982 to 2020.Subsequently,we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998.Additionally,we developed an Afforestation Efficiency Index(AEI)to quantitatively assess the cost efficiency of afforestation projects.Results indicated that forest in the UYRB showed sustained increases during 1982-2020,with most areas experiencing greater growth after 1998 than before.Temporal effects of climatic factors influenced over 42.7%of the forest,and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61-24.73%.Human activities emerged as the dominant driver of forest dynamics post 1998,whereas climate variables predominated before this period.The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high,with higher success predominantly observed in the northeastern and eastern counties,while the central,western,and northwestern counties mainly showed relatively low efficiency.These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives,and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.展开更多
This study aims to explore how the Wei River Basin can enhance the efficiency of horizontal ecological compensation to promote high-quality and sustainable development in the Yellow River Basin.To achieve this,a four-...This study aims to explore how the Wei River Basin can enhance the efficiency of horizontal ecological compensation to promote high-quality and sustainable development in the Yellow River Basin.To achieve this,a four-stage DEA(Data Envelopment Analysis)method was employed to evaluate the efficiency of ecological compensation in six prefecture-level cities within the Wei River Basin from 2001 to 2022.In addition,the K-prototype clustering analysis method was integrated to assess the regional differences in ECE(ecological compensation efficiency).The findings indicate:(1)the ecological compensation efficiency in the upstream areas of the Wei River Basin is significantly higher than in the downstream regions;(2)the influence of factors such as the proportion of the tertiary industry,population density and residents’disposable income on the efficiency of ecological compensation is significant;(3)after excluding environmental factors,the overall ecological compensation efficiency showed a significant improvement.Based on these insights,it is recommended that the provinces of Shaanxi and Gansu further establish a robust compensation fund operation mechanism,build a cross-regional ecological compensation upstream-downstream coordination system,and strengthen inter-basin economic cooperation mechanisms to promote dual-driven development through technological advancement and scale benefits,thereby advancing ecological protection and sustainable development in the Wei River Basin.展开更多
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
Towards the development of highly efficient electrochromic coatings,the crystallinity,morphology(e.g.size and shape)of electrochromic nanomaterials,and their charge insertion capacities play a significant role.Herein,...Towards the development of highly efficient electrochromic coatings,the crystallinity,morphology(e.g.size and shape)of electrochromic nanomaterials,and their charge insertion capacities play a significant role.Herein,we report the structure-dependent colouration effciency in electrochromic coatings based on the use of 0D,1D and 2D tungsten trioxide(WO_(3))nanostructures.A series of WO_(3)with different nanostructures were prepared and used as working electrodes to fabricate electrochromic devices for smart windows applications.Facile spray coating was applied on fluorine-doped tin oxide(FTO)substrate to make~70%transparent working electrodes to investigate their charge insertion capacities,electrochromic active surface area,and colouration efficiency.Results showed that the 2D WO_(3)nanoflakes displayed the highest diffusion coefficient for the intercalation of 1.52×10^(-10)cm^(2)/s with an increased electrochemical active surface area of 25.10 mF/cm^(2),a large modulation of optical reflectance(42.63%)with 3.79 s shorter response time for bleaching and a greater colouration efficiency(CE)value(89.29 cm^(2)/C)at 700 nm compared to the CE value for 1D WO_(3)(of 22 cm^(2)/C)and 0D WO_(3)(8 cm^(2)/C).The outcome of this study provides a new insight and valuable contribution to design an efficient electrochromic coating by controlling and optimising the nanostructures of selective electrochromic materials.展开更多
The effects of micro-ridge-furrow planting(MR)on yield and the efficiency of light,water,and thermal resource use in rapeseed were tested in a three-year field experiment comparing MR to conventional flat planting.MR ...The effects of micro-ridge-furrow planting(MR)on yield and the efficiency of light,water,and thermal resource use in rapeseed were tested in a three-year field experiment comparing MR to conventional flat planting.MR enhanced canopy heterogeneity by altering the leaf angle between plants on ridges and furrows.The heterogeneous canopy environment increased intercepted photosynthetic active radiation,alleviated canopy temperature stress,and optimized canopy humidity,leading to improvements in light-nitrogen matching and net photosynthetic rate.Consequently,dry matter and yield increased by 13.0%and 11.0%,respectively,while radiation,thermal,and precipitation utilization efficiency increased by 12.3%-16.2%.The corresponding improvements in yield and resource use efficiency were attributed to a heterogeneous canopy environment that improved microclimatic conditions.展开更多
Oil and gas pipelines are a vital long-distance liquid and natural gas carrier,but their functionality is being assessed from a two-fold perspective of power economy and environmentalism.This review concurs on the way...Oil and gas pipelines are a vital long-distance liquid and natural gas carrier,but their functionality is being assessed from a two-fold perspective of power economy and environmentalism.This review concurs on the way these outcomes are interdependent throughout the pipeline lifecycle by contending that the efficiency,emissions,reliability,and environmental risk are jointly determined through the shared design decisions,operating plans,integrity platforms,and monitoring and response plans.Our initial conceptualization is pipeline systems and performance measures,which are characterized by boundary and comparability issues of particular energy consumption,methane intensity,and release consequence measures.Next,we look at hydraulic and station optimization,focusing on the need to look at the importance of equipment performance at part loads,constraints consciousness dispatch,and transient management to prevent the erosion of integrity levels by efficiency gains.The integrity management is appraised as one of the key enablers of stewardship that connects the corrosion prevention,in-line inspection and verification,and the risk-based mitigation to less likely failure,less disruptive interventions,and reduced emissions during maintenance.We compare the leak and spill prevention,detection,quantification,and response of the SCADA(supervisory control and data acquisition)-based computational monitoring,distributed sensing,as well as aerial/satellite,focusing on the validation,characterization of uncertainty,and the operational parameters modulating the time-to-detect and isolation performance.Environmental impacts of the lifecycle,not related to releases,are explained,such as routing and construction disturbance,management of right-of-way,station externalities,decommissioning,and climate resilience.Lastly,we assess new technologies,such as continuous monitoring networks,electrification,superior materials,and multi-objective decision-making that collaborates to increase energy,reliability,and environmental performance in heterogeneous pipeline networks.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62171204,62171129,62001192).
文摘We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulation characteristics of CB-RLM PAs,a comprehensive objective function is proposed that combines multi-state impedance trajectory constraints with in-band performance deviations.For the saturation and 6 dB power back-off(PBO)states,approximately optimal impedance regions on the Smith chart are derived using impedance constraint circles based on load-pull simulations.These regions are used together with in-band performance deviations(e.g.,saturated efficiency,6 dB PBO efficiency,and saturated output power)for matching network optimization and design.Second,a multi-objective evolutionary algorithm based on decomposition with adaptive weights,neighborhood,and global replacement is integrated with harmonic balance simulations to optimize design parameters and evaluate performance.Finally,to validate the proposed method,a broadband CB-RLM PA operating from 0.6 to 1.8 GHz is designed and fabricated.Measurement results show that the efficiencies at saturation,6 dB PBO,and 8 dB PBO all exceed 43.6%,with saturated output power being maintained at 40.9–41.5 dBm,which confirms the feasibility and effectiveness of the proposed broadband high-efficiency CB-RLM PA optimization and design approach.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
基金supported by the National Natural Science Foundation of China(32071960)the National Key Research and Development Program of China(2018YFD0300603)。
文摘In maize production,the development of density-tolerant and lodging-resistant varieties has made dense planting an effective strategy for achieving high and stable yields,with superior hybrids serving as a prerequisite for successful highdensity cultivation.However,the photosynthetic mechanisms underlying improved density tolerance in maize hybrids released across different eras in China remain unclear.This study investigates 40 years of breeding progress toward enhanced photosynthetic traits under varying planting densities and elucidates the physiological and ecological bases of improved density tolerance in maize hybrids.A three-year field experiment was conducted from 2019 to 2021 to compare eight major Chinese hybrids from four decadal cohorts under three planting densities:45,000(D1),67,500(D2),and 90,000(D3)plants ha^(-1).At high density(D3),modern hybrids exhibited a more optimal canopy architecture and superior leaf photosynthetic performance compared to older hybrids,despite a slight reduction in specific leaf nitrogen.Notably,modern hybrids(2000s)were able to maintain higher net photosynthetic rates and photosynthetic nitrogen use efficiency(PNUE)at D3,resulting in the highest grain yield(GY),which was 118.47%greater than that of older hybrids(1970s).Leaf area duration after anthesis,total chlorophyll content,key photosynthetic enzyme activities,and maximum quantum efficiency of PSII photochemistry were all positively correlated with GY.Among these,PNUE showed the strongest correlation with grain yield and thus represents a key indicator for optimizing maize hybrids.Based on these findings,breeders should continue selecting hybrids under high-density and suboptimal conditions,focusing on optimizing population architecture and enhancing photosynthetic capacity while fine-tuning leaf nitrogen status to develop high-yielding,density-tolerant hybrids capable of sustaining long-term increases in maize grain yield.
基金supported by the National Key Research and Development Program of China(2022YFD2300304)the National Natural Science Foundation of China(32272197 and 32071944)+2 种基金the Hong Kong Research Grants Council,China(GRF 14177617,12103219,12103220,and AoE/M-403/16)the State Key Laboratory of Agrobiotechnology(Strategic Collaborative Projects)at the Chinese University of Hong Kong,Chinathe Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)。
文摘Lodging is a primary factor limiting rice grain yield.Achieving synergistic improvements in grain yield and nitrogen use efficiency(NUE)without increasing lodging risk has been a global research priority.In this study,two rice cultivars-Yongyou 2640(indica–japonica hybrid rice)and Jinxiangyu 1(inbred japonica rice)-were evaluated in field experiments conducted over two growing seasons.Six nitrogen management strategies were implemented:no nitrogen(T1),conventional urea(T2),controlled-release nitrogen(CRN)(T3),reduction of CRN(T4),CRN combined with single basal application of conventional urea(T5),and CRN combined with split applications of conventional urea(T6).Compared with T2,the integrated nitrogen strategies(T5 and T6)increased NUE by 4.89–5.69%and grain yield by 3.41–4.65%.These treatments also enhanced structural integrity of the second basal internode,evidenced by increased carbohydrate content,internode breaking strength,epidermal silicon layer thickness,number of large and small vascular bundles,and thickness of both parenchymatous and mechanical tissues.Concurrently,internode length,bending moment,and lodging index were reduced.Collectively,these findings indicate that integrating CRN with conventional urea improves morphological,mechanical,physicochemical,and anatomical properties of the second basal internode,thereby enhancing stem strength and enabling high yield and NUE without compromising lodging resistance.
基金supported by the Hubei Provincial Science and Technology Project,China(2025CSA039)the National Natural Science Foundation of China(32001467)。
文摘Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting density and N rate,and their relationships with grain yield,radiation use efficiency(RUE),and N use efficiency for grain production(NUEg)in rice.A two-year field experiment was conducted with two hybrid varieties under three N levels,0 kg ha^(-1)(N1),90 kg ha^(-1)(N2)and 180 kg ha^(-1)(N3),and two planting densities,22.2 hills m-2(D1)and 33.3 hills m^(-2)(D2).Results showed 3.4%higher yield and 4.4%higher NUEg under N2D2 compared with N3D1.The extinction coefficient for N(K_(N))and light(K_(L))and their ratio(K_(N)/K_(L))at heading stage were significantly influenced by N rate,planting density,and their interaction.K_(N)decreased with the increase of N input or planting density.Compared to N1,K_(N)decreased by 43.5 and 58.8%under N2 and N3,respectively,while K_(N)under D2 decreased by 16.0%compared to D1.Higher K_(L)and K_(N)/K_(L)values occurred under low N rates,with opposite trends under high N rates.Increased planting density led to decreased K_(L)and K_(N)/K_(L)values.N2D2 demonstrated higher K_(L)and K_(N),and thus comparable K_(N)/K_(L),compared to N3D1.Correlation analysis revealed K_(L)negatively correlated with RUE,while K_(N)and K_(N)/K_(L)positively correlated with NUEg.These findings indicate that increasing planting density under reduced N input could maintain rice yield while enhancing resource use efficiency through regulation of canopy light and N distribution.
基金supported by the National Key R&D Program of China(No.2021ZD0112700).
文摘This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.
基金supported in part by the Opening Project of Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory under Grant AD25069102in part by the Basic Ability Improvement Project of Young and Middle Aged Teachers in Guangxi Universities,under Grant 2023KY0226+6 种基金in part by Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education of China,underGrant CRKL220108in part by the Innovation Project of Guangxi Graduate Education,under Grant YCBZ2023131in part by the Doctoral Research Foundation of Guilin University of Electronic Technology,under Grant UF23038Yin part by the Bagui Youth Top Talent Projectin part by the Guangxi Key Research and Development Program under Grant AB25069510in part by Open Fund of IPOC(BUPT),No.IPOC2024B07in part by Guangxi Key Laboratory of Precision Navigation Technology and Application,under Grant DH202309.
文摘UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple UAVs-mounted IRS(U-IRS),where the data from ground SNs are transmitted to the FC.In practice,energy efficiency(EE)and mission completion time are crucial metrics for evaluating system performance and operational costs.Recognizing their importance during data collection,we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously.To characterize this tradeoff while considering optimization objective consistency,we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE.Due to the non-convex nature of the formulated problem,obtaining optimal solutions is generally challenging.To tackle this issue,we decompose it into three subproblems:UAV-SN association,number of reflecting elements allocation,andUAVtrajectory optimization.An iterative algorithmcombining genetic algorithm,CS-BJ algorithm,and successive convex approximation technique is proposed to solve these sub-problems.Simulation results demonstrate that when the transmitted data amount is 10 and 30Mbits,compared to the static collection benchmark(the UAV hovers directly above each SN),the EE of the proposed method improves by more than 10.4% and 5.2%,while the total mission completion time is reduced by more than 5.4% and 3.3%,respectively.
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
文摘Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the National Natural Science Foundation of China(Nos.52475317 and 52305331).
文摘In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components,which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-Ⅱ algorithm.The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation,while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction,establishing the hybrid physics-informed data method for WAAM microstructure prediction.The optimized process achieved a deposition rate of 6257 mm3/min,with effective width and average layer height maintained at 10.1 mm and 4.13 mm,respectively.Microstructural optimization produced a fine,uniform,fully equiaxed grain structure with an average grain size of 38μm.These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight,high-performance components in aerospace and transportation sectors.
基金supported in part by the National Natural Science Foundation of China under Grant 62071253,Grant 62371252 and Grant 62271268in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2022800in part by the Jiangsu Provincial 333 Talent Project.
文摘In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.
基金The funding for this publication was provided by Johannes Kepler University(JKU),Linz.Special thanks to Prof.Zongmin DENG from Beihang University for his invaluable guidance,insightful feedback,and constructive criticism,which greatly enhanced the quality of this manuscript.We extend our heartfelt gratitude to the PARSIFAL team for providing the supporting materials,which inspired this study.
文摘The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.
文摘Tourism’s link to the Sustainable Development Goals has been a continuing emphasis,adding momentum to longstandingefforts to ensure tourism’s sustainability.Tourism transport is one of the largest sources of anthropogenic carbonemissions,driving global ecological change with profound consequences for ecosystem functioning and biodiversity.Large-scale infrastructure projects such as railway expansion are increasingly promoted for their potential to reducetourism-related carbon dioxide emissions,yet their spatial ecological impacts on regional carbon cycles and ecosystemservices remain poorly understood.This study introduces the concept of Tourism Transport Ecological Efficiency(TTEE)to assess the relationship between human infrastructure,carbon emissions,and ecological sustainability.Using panel datafrom China’s railway expansion between 2011 and 2018,the study provides spatially explicit evidence of how transportinfrastructure shapes tourism’s ecological footprint.Results show that non-Eastern regions experienced a greater increasein TTEE(8.7%)compared to Eastern regions(5.5%),highlighting regional disparities in tourism transport ecologicalsustainability.Railway density had a significant positive direct effect on TTEE,particularly pronounced in non-Easternregions.Additionally,a significant indirect effect of railway density in nearby regions was identified.These findings revealthe interconnected ecological impacts of transport systems and underscore the importance of regionally targeted railwayinvestment strategies.By bridging infrastructure development with ecological processes,this study advances understandingof how tourism transport can be aligned with global carbon reduction goals and ecosystem protection.
基金supported by the National Natural Science Foundation of China(42071238)the Jiuzhaigou Post-Disaster Restoration and Reconstruction Program(5132202020000046)+1 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)the Ministry of Science and Technology of the People's Republic of China(2019QZKK0402)。
文摘Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost inputs.This gap can lead to increased uncertainties in restoration planning.Here we investigated forest dynamics in China's Upper Yangtze River Basin(UYRB)using kernel Normalized Difference Vegetation Index(kNDVI),Leaf Area Index(LAI),Gross Primary Productivity(GPP),Ku-band Vegetation Optical Depth(Ku-VOD)time series and climate data from1982 to 2020.Subsequently,we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998.Additionally,we developed an Afforestation Efficiency Index(AEI)to quantitatively assess the cost efficiency of afforestation projects.Results indicated that forest in the UYRB showed sustained increases during 1982-2020,with most areas experiencing greater growth after 1998 than before.Temporal effects of climatic factors influenced over 42.7%of the forest,and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61-24.73%.Human activities emerged as the dominant driver of forest dynamics post 1998,whereas climate variables predominated before this period.The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high,with higher success predominantly observed in the northeastern and eastern counties,while the central,western,and northwestern counties mainly showed relatively low efficiency.These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives,and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.
基金funded by the Gansu Soft Science Planning Project(Grant No.25JRZA170).
文摘This study aims to explore how the Wei River Basin can enhance the efficiency of horizontal ecological compensation to promote high-quality and sustainable development in the Yellow River Basin.To achieve this,a four-stage DEA(Data Envelopment Analysis)method was employed to evaluate the efficiency of ecological compensation in six prefecture-level cities within the Wei River Basin from 2001 to 2022.In addition,the K-prototype clustering analysis method was integrated to assess the regional differences in ECE(ecological compensation efficiency).The findings indicate:(1)the ecological compensation efficiency in the upstream areas of the Wei River Basin is significantly higher than in the downstream regions;(2)the influence of factors such as the proportion of the tertiary industry,population density and residents’disposable income on the efficiency of ecological compensation is significant;(3)after excluding environmental factors,the overall ecological compensation efficiency showed a significant improvement.Based on these insights,it is recommended that the provinces of Shaanxi and Gansu further establish a robust compensation fund operation mechanism,build a cross-regional ecological compensation upstream-downstream coordination system,and strengthen inter-basin economic cooperation mechanisms to promote dual-driven development through technological advancement and scale benefits,thereby advancing ecological protection and sustainable development in the Wei River Basin.
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
基金the funding by the ARC Research Hub for Advanced Manufacturing with 2D Materials(ARC IH210100025)。
文摘Towards the development of highly efficient electrochromic coatings,the crystallinity,morphology(e.g.size and shape)of electrochromic nanomaterials,and their charge insertion capacities play a significant role.Herein,we report the structure-dependent colouration effciency in electrochromic coatings based on the use of 0D,1D and 2D tungsten trioxide(WO_(3))nanostructures.A series of WO_(3)with different nanostructures were prepared and used as working electrodes to fabricate electrochromic devices for smart windows applications.Facile spray coating was applied on fluorine-doped tin oxide(FTO)substrate to make~70%transparent working electrodes to investigate their charge insertion capacities,electrochromic active surface area,and colouration efficiency.Results showed that the 2D WO_(3)nanoflakes displayed the highest diffusion coefficient for the intercalation of 1.52×10^(-10)cm^(2)/s with an increased electrochemical active surface area of 25.10 mF/cm^(2),a large modulation of optical reflectance(42.63%)with 3.79 s shorter response time for bleaching and a greater colouration efficiency(CE)value(89.29 cm^(2)/C)at 700 nm compared to the CE value for 1D WO_(3)(of 22 cm^(2)/C)and 0D WO_(3)(8 cm^(2)/C).The outcome of this study provides a new insight and valuable contribution to design an efficient electrochromic coating by controlling and optimising the nanostructures of selective electrochromic materials.
基金supported by the National Key Research and Development Program of China (2021YFD1901200)the Key Research and Development Program of Hubei Province of China (2023BBB028)+1 种基金the Earmarked Fund of Hubei province of Chinathe Fundamental Research Funds for the Central Universities (2662024ZKQD005)
文摘The effects of micro-ridge-furrow planting(MR)on yield and the efficiency of light,water,and thermal resource use in rapeseed were tested in a three-year field experiment comparing MR to conventional flat planting.MR enhanced canopy heterogeneity by altering the leaf angle between plants on ridges and furrows.The heterogeneous canopy environment increased intercepted photosynthetic active radiation,alleviated canopy temperature stress,and optimized canopy humidity,leading to improvements in light-nitrogen matching and net photosynthetic rate.Consequently,dry matter and yield increased by 13.0%and 11.0%,respectively,while radiation,thermal,and precipitation utilization efficiency increased by 12.3%-16.2%.The corresponding improvements in yield and resource use efficiency were attributed to a heterogeneous canopy environment that improved microclimatic conditions.
文摘Oil and gas pipelines are a vital long-distance liquid and natural gas carrier,but their functionality is being assessed from a two-fold perspective of power economy and environmentalism.This review concurs on the way these outcomes are interdependent throughout the pipeline lifecycle by contending that the efficiency,emissions,reliability,and environmental risk are jointly determined through the shared design decisions,operating plans,integrity platforms,and monitoring and response plans.Our initial conceptualization is pipeline systems and performance measures,which are characterized by boundary and comparability issues of particular energy consumption,methane intensity,and release consequence measures.Next,we look at hydraulic and station optimization,focusing on the need to look at the importance of equipment performance at part loads,constraints consciousness dispatch,and transient management to prevent the erosion of integrity levels by efficiency gains.The integrity management is appraised as one of the key enablers of stewardship that connects the corrosion prevention,in-line inspection and verification,and the risk-based mitigation to less likely failure,less disruptive interventions,and reduced emissions during maintenance.We compare the leak and spill prevention,detection,quantification,and response of the SCADA(supervisory control and data acquisition)-based computational monitoring,distributed sensing,as well as aerial/satellite,focusing on the validation,characterization of uncertainty,and the operational parameters modulating the time-to-detect and isolation performance.Environmental impacts of the lifecycle,not related to releases,are explained,such as routing and construction disturbance,management of right-of-way,station externalities,decommissioning,and climate resilience.Lastly,we assess new technologies,such as continuous monitoring networks,electrification,superior materials,and multi-objective decision-making that collaborates to increase energy,reliability,and environmental performance in heterogeneous pipeline networks.
